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RESEARCH PAPER · Angewandte Chemie International Edition · 2019

Autonomous Discovery in the Chemical Sciences Part II: Outlook

Connor W. Coley, Natalie S. Eyke, Klavs F. Jensen

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Autonomous Discovery in the Chemical Sciences Part II: OutlookOriginalAutonomous Discovery in the Chemical Sciences Part I: Progress317Artificial General Intelligence: A New Perspective, with Application to Scientific Discovery3AI-assisted synthesis prediction54Benchmarking the acceleration of materials discovery by sequential learning149Automated, Accelerated Nanoscale Synthesis of Iminopyrrolidines33Current and Future Roles of Artificial Intelligence in Medicinal Chemistry Synthesis243Inverse methods for design of soft materials94Learning To Navigate The Synthetically Accessible Chemical Space Using Reinforcement Learning50Recent progress on discovery and properties prediction of energy materials: Simple machine learning meets complex quantum chemistry62Role of Artificial Intelligence and Machine Learning in Nanosafety175Automation of Synthesis in Medicinal Chemistry: Progress and Challenges35A generalized benders decomposition-based global optimization approach to symbolic regression for explicit surrogate modeling from limited data information7Designing and understanding light-harvesting devices with machine learning112Flow Synthesis of Metal Halide Perovskite Quantum Dots: From Rapid Parameter Space Mapping to AI-Guided Modular Manufacturing84Adaptive Optimization of Chemical Reactions with Minimal Experimental Information82Machine learning meets mechanistic modelling for accurate prediction of experimental activation energies204Deep Learning for Prediction and Optimization of Fast-Flow Peptide Synthesis60Deep Learning in Protein Structural Modeling and Design226A Recommender System for Inverse Design of Polycarbonates and Polyesters28Solvent Selection for Mitsunobu Reaction Driven by an Active Learning Surrogate Model24Deep learning and generative methods in cheminformatics and chemical biology: navigating small molecule space intelligently55Defining and Exploring Chemical Spaces124Regio-selectivity prediction with a machine-learned reaction representation and on-the-fly quantum mechanical descriptors169Accelerating the optimization of enzyme-catalyzed synthesis conditions via machine learning and reactivity descriptors9Chemical data intelligence for sustainable chemistry68Combining machine learning and high-throughput experimentation to discover photocatalytically active organic molecules113Data-driven machine learning model for the prediction of oxygen vacancy formation energy of metal oxide materials31Golem: an algorithm for robust experiment and process optimization38Toward autonomous design and synthesis of novel inorganic materials159Toward Machine Learning-Enhanced High-Throughput Experimentation155Future medicinal chemists experience flow chemistry: optimization by experimental design of the limiting synthetic step to the antifungal drug econazole nitrate3Machine learning, artificial intelligence, and data science breaking into drug design and neglected diseases62Deep Generative Models Enable Navigation in Sparsely Populated Chemical Space4Comment on acp-2020-12580Comment on acp-2020-12580Automated robotic platforms in design and development of formulations22Gemini: Dynamic Bias Correction for Autonomous Experimentation and Molecular Simulation1Ready, Set, Flow! Automated Continuous Synthesis and Optimization134Olympus: a benchmarking framework for noisy optimization and experiment planning69Organic reactivity from mechanism to machine learning175Comment on acp-2020-12580Toward Building Science Discovery Machines0Predicting Reaction Yields via Supervised Learning187Machine learning the quantum-chemical properties of metal–organic frameworks for accelerated materials discovery538Isolated metal atoms and clusters for alkane activation: Translating knowledge from enzymatic and homogeneous to heterogeneous systems43Reply on RC10Reply on RC20Reply on RC30Inferring experimental procedures from text-based representations of chemical reactions80Using a genetic algorithm to find molecules with good docking scores38GenUI: Interactive and Extensible Open Source Software Platform for De Novo Molecular Generation0GenUI: Interactive and Extensible Open Source Software Platform for De Novo Molecular Generation0De novo molecular design and generative 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cross‐couplings37GenUI: interactive and extensible open source software platform for de novo molecular generation and cheminformatics21Designing small molecules for therapeutic success: A contemporary perspective36An In-depth Summary of Recent Artificial Intelligence Applications in Drug Design3Amortized Tree Generation for Bottom-up Synthesis Planning and Synthesizable Molecular Design19Enabling modular autonomous feedback-loops in materials science through hierarchical experimental laboratory automation and orchestration5Recent advances in autonomous synthesis of materials33The Evolution of Data-Driven Modeling in Organic Chemistry185Transition to sustainable chemistry through digitalization85NEXTorch: A Design and Bayesian Optimization Toolkit for Chemical Sciences and Engineering82Accelerate Synthesis of Metal–Organic Frameworks by a Robotic Platform and Bayesian Optimization77Automated high throughput pKa and distribution coefficient measurements of pharmaceutical compounds for the SAMPL8 blind prediction challenge15Multi-swarm UPSO algorithm based on seed strategy for atomic clusters structure optimization1High‐Throughput Experimentation and Computational Freeway Lanes for Accelerated Battery Electrolyte and Interface Development Research140Modern nanoscience: Convergence of AI, robotics, and colloidal synthesis45Implications of the BATTERY 2030+ AI‐Assisted Toolkit on Future Low‐TRL Battery Discoveries and Chemistries65Rechargeable Batteries of the Future—The State of the Art from a BATTERY 2030+ Perspective376Progress towards machine learning reaction rate constants58Optimizing molecules using efficient queries from property evaluations74Artificial intelligence–enabled fuel design2Bayesian optimization with known experimental and design constraints for chemistry applications89Continuous stirred-tank reactor cascade platform for self-optimization of reactions involving solids50Hyperconverged autonomous organic reaction infrastructure (HAORI) driven by SpecSNN, for low dielectric constant polymer research2Limitations of machine learning models when predicting compounds with completely new chemistries: possible improvements applied to the discovery of new non-fullerene acceptors39Machine learning the quantum flux–flux correlation function for catalytic surface reactions1Prospects and challenges for autonomous catalyst discovery viewed from an experimental perspective46Enabling Modular Autonomous Feedback‐Loops in Materials Science through Hierarchical Experimental Laboratory Automation and Orchestration73From Platform to Knowledge Graph: Evolution of Laboratory Automation108A Comprehensive Discovery Platform for Organophosphorus Ligands for Catalysis309Machine Learning Assisted Design of Experiments for Solid State Electrolyte Lithium Aluminum Titanium Phosphate24Towards artificial intelligence at scale in the chemical industry69Autonomous platforms for data-driven organic synthesis68Data-Driven Design and Autonomous Experimentation 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Ligand Design133Graph-based Automated Macro-Molecule Assembly0Autonomous Chemical Experiments: Challenges and Perspectives on Establishing a Self-Driving Lab294Brokering between tenants for an international materials acceleration platform5Recent Developments in Process Digitalisation for Advanced Nanomaterial Syntheses11Bridging Chemical Knowledge and Machine Learning for Performance Prediction of Organic Synthesis51Machine Learning Guides Peptide Nucleic Acid Flow Synthesis and Sequence Design22Efficient hyperparameter tuning for kernel ridge regression with Bayesian optimization104Toward autonomous laboratories: Convergence of artificial intelligence and experimental automation118A semi-automated material exploration scheme to predict the solubilities of tetraphenylporphyrin derivatives7Predictive chemistry: machine learning for reaction deployment, reaction development, and reaction discovery138Active Learning Exploration of Transition-Metal Complexes to Discover Method-Insensitive and Synthetically Accessible Chromophores37What is a minimal working example for a self-driving laboratory?49Autonomous Drug Design with Multi-Armed Bandits7Research Acceleration in Self‐Driving Labs: Technological Roadmap toward Accelerated Materials and Molecular Discovery51A field guide to flow chemistry for synthetic organic chemists393Autonomous kinetic model identification using optimal experimental design and retrospective data analysis: methane complete oxidation as a case study13Definition and exploration of realistic chemical spaces using the connectivity and cyclic features of ChEMBL and ZINC10Equipping data-driven experiment planning for Self-driving Laboratories with semantic memory: case studies of transfer learning in chemical reaction optimization33First principles reaction discovery: from the Schrodinger equation to experimental prediction for methane pyrolysis48Integrating autonomy into automated research platforms32Machine Learning for Protein Engineering19Machine learning integrated photocatalysis: progress and challenges94Synthesize in a Smart Way: A Brief Introduction to Intelligence and Automation in Organic Synthesis1Synthetic data enable experiments in atomistic machine learning16A Review on Artificial Intelligence Enabled Design, Synthesis, and Process Optimization of Chemical Products for Industry 4.076Enabling technology and core theory of synthetic biology65Open-Source Chromatographic Data Analysis for Reaction Optimization and Screening34Quantum machine learning framework for virtual screening in drug discovery: a prospective quantum advantage73Self-driving laboratories: A paradigm shift in nanomedicine development84AlphaFlow: autonomous discovery and optimization of multi-step chemistry using a self-driven fluidic lab guided by reinforcement learning215Recent advances in computational modeling of MOFs: From molecular simulations to machine learning217Artificial intelligence in molecular de novo design: Integration with experiment23Bimetallic Sites for Catalysis: From Binuclear Metal Sites to Bimetallic Nanoclusters and Nanoparticles689Autonomous experimental systems in materials science22In-line small high-pressure sensors in anodically bonded microfluidic restrictors1Olympus, enhanced: benchmarking mixed-parameter and multi-objective optimization in chemistry and materials science8Generative Models Should at Least Be Able to Design Molecules That Dock Well: A New Benchmark69Bringing a Mechanistic Lens to the Development of New Transfer Hydroarylation Isodesmic Reactions for the Synthesis of Amides, Thioamides, Amidines, Alkenes and Ketones from Carboxylic Acids via Extrusion and Insertion Elementary Steps6Achieving Digital Catalysis: Strategies for Data Acquisition, Storage and Use37Achieving Digital Catalysis: Strategies for Data Acquisition, Storage and Use6Deriving equation from data via knowledge discovery and machine learning: A study of Young’s modulus of Ti-Nb alloys8Integrating model-based design of experiments and computer-aided solvent design7

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  1. Autonomous Discovery in the Chemical Sciences Part I: Progress

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  2. Artificial General Intelligence: A New Perspective, with Application to Scientific Discovery

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  3. AI-assisted synthesis prediction

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  4. Benchmarking the acceleration of materials discovery by sequential learning

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  5. Automated, Accelerated Nanoscale Synthesis of Iminopyrrolidines

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  6. Current and Future Roles of Artificial Intelligence in Medicinal Chemistry Synthesis

    Journal of Medicinal Chemistry · 2020-04-03

    Thomas J. Struble, Juan C. Alvarez, Scott P. Brown, Milan Chytil, Justin S. Cisar, Renée L. DesJarlais, Ola Engkvist, Scott A. Frank, Daniel R. Greve, Daniel J. Griffin, Xinjun Hou, Jeffrey W. Johannes, Constantine Kreatsoulas, Brian R. Lahue, Miriam Mathea, Georg Mogk, Christos A. Nicolaou, Andrew Palmer, Daniel J. Price, Richard I. Robinson, Sebastian Salentin, Xing Li, Tommi Jaakkola, William H. Green, Regina Barzilay, Connor W. Coley, Klavs F. Jensen

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  7. Inverse methods for design of soft materials

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  8. Learning To Navigate The Synthetically Accessible Chemical Space Using Reinforcement Learning

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  9. Recent progress on discovery and properties prediction of energy materials: Simple machine learning meets complex quantum chemistry

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  10. Role of Artificial Intelligence and Machine Learning in Nanosafety

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  11. Automation of Synthesis in Medicinal Chemistry: Progress and Challenges

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  12. A generalized benders decomposition-based global optimization approach to symbolic regression for explicit surrogate modeling from limited data information

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  13. Designing and understanding light-harvesting devices with machine learning

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  14. Flow Synthesis of Metal Halide Perovskite Quantum Dots: From Rapid Parameter Space Mapping to AI-Guided Modular Manufacturing

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  15. Adaptive Optimization of Chemical Reactions with Minimal Experimental Information

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  16. Machine learning meets mechanistic modelling for accurate prediction of experimental activation energies

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  17. Deep Learning for Prediction and Optimization of Fast-Flow Peptide Synthesis

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  18. Deep Learning in Protein Structural Modeling and Design

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  19. A Recommender System for Inverse Design of Polycarbonates and Polyesters

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  20. Solvent Selection for Mitsunobu Reaction Driven by an Active Learning Surrogate Model

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  21. Deep learning and generative methods in cheminformatics and chemical biology: navigating small molecule space intelligently

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  22. Defining and Exploring Chemical Spaces

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  23. Regio-selectivity prediction with a machine-learned reaction representation and on-the-fly quantum mechanical descriptors

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  24. Accelerating the optimization of enzyme-catalyzed synthesis conditions via machine learning and reactivity descriptors

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  25. Chemical data intelligence for sustainable chemistry

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  26. Combining machine learning and high-throughput experimentation to discover photocatalytically active organic molecules

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  27. Data-driven machine learning model for the prediction of oxygen vacancy formation energy of metal oxide materials

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  28. Golem: an algorithm for robust experiment and process optimization

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  29. Toward autonomous design and synthesis of novel inorganic materials

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  31. Future medicinal chemists experience flow chemistry: optimization by experimental design of the limiting synthetic step to the antifungal drug econazole nitrate

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  32. Machine learning, artificial intelligence, and data science breaking into drug design and neglected diseases

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  33. Deep Generative Models Enable Navigation in Sparsely Populated Chemical Space

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  34. Comment on acp-2020-1258

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  35. Comment on acp-2020-1258

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  36. Automated robotic platforms in design and development of formulations

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  37. Gemini: Dynamic Bias Correction for Autonomous Experimentation and Molecular Simulation

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  38. Ready, Set, Flow! Automated Continuous Synthesis and Optimization

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  39. Olympus: a benchmarking framework for noisy optimization and experiment planning

    Machine Learning Science and Technology · 2021-03-11

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  40. Organic reactivity from mechanism to machine learning

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  41. Comment on acp-2020-1258

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  42. Toward Building Science Discovery Machines

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  43. Predicting Reaction Yields via Supervised Learning

    Accounts of Chemical Research · 2021-03-31

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  44. Machine learning the quantum-chemical properties of metal–organic frameworks for accelerated materials discovery

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  45. Isolated metal atoms and clusters for alkane activation: Translating knowledge from enzymatic and homogeneous to heterogeneous systems

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  46. Reply on RC1

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  47. Reply on RC2

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  48. Reply on RC3

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  49. Inferring experimental procedures from text-based representations of chemical reactions

    Nature Communications · 2021-05-06

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  50. Using a genetic algorithm to find molecules with good docking scores

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